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NAML 2021

Next Generation NLP and AI Methods in the Navy Contact Center

help desk

contact center

call center

natural language processing

unsupervised

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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poster

A heterogeneous, nonoverlapping domain-decomposition, explicit, finite volume method (HT-NODDE-FVM) is developed and applied to a real-time, hybrid, process-state estimator of 3D unsteady, advection-diffusion plume fields produced by moving aerial intruders. The hybrid Luenberger-naïve estimator uses field measurements taken onboard a data-driven self-guided UAV and computes the plume field in real-time while guiding the UAV to locations that provide optimal information to the estimator.

Real-Time Estimation of Threat Fields With an Autodidactic Data-Driven Sensing Aerial Vehicle

The search for neural architecture is producing many of the most exciting results in artificial intelligence. It has increasingly become apparent that task-specific neural architecture plays a crucial role for effectively solving problems. This paper presents a simple method for learning neural architecture through random mutation. This method demonstrates 1) neural architecture may be learned during the agent’s lifetime, 2) neural architecture may be constructed over a single lifetime without any initial connections or neurons, and 3) architectural modifications enable rapid adaptation to dynamic and novel task scenarios. Starting without any neurons or connections, this method constructs a neural architecture capable of high-performance on several tasks. The lifelong learning capabilities of this method are demonstrated in an environment without episodic resets, even learning with constantly changing morphology, limb disablement, and changing task goals all without losing locomotion capabilities.

Self-Constructing Neural Networks Through Random Mutation

Object proposal algorithms are needed to find bounding boxes for salient class-agnostic objects. It is an important pre-processing step for object detection in the wild. While most state-of-the-art object detection methods adopt an end-to-end deep neural networks, here we aim at an independent object proposal unit that has low complexity and high performance. The proposed light-weight object proposal can be combined with any classification process to reduce model and computation complexity.
Our object proposal method is built upon the PixelHop framework, it is called OPPHop (Object Proposal PixelHop). The term “hop” denotes the neighborhood of a pixel. The high-level principle of our method is described below. OPPHop conducts spectral analysis on neighborhoods of different sizes centered on a pixel through a sequence of cascaded dimension reduction units. The spectral analysis tool is called the Saab (Subspace approximation with adjusted bias) transform. The neighborhoods of an object contain salient contours and, as a result, they have distinctive spectral signatures at a certain scale that matches the object size. The distinctive regions can be predicted based on supervised learning with Saab coefficients as the input.
The proposed OPPHop method consists of the following three steps: 
1. From fine-to-coarse spatial resolutions, conduct successive Saab transforms for spectral representation of neighborhoods of various sizes.
2. Predict distinctive spatial regions in various spatial resolutions via supervised learning.
3. Fuse distinctive regions of different spatial resolutions via image priors.
We evaluate OPPHop framework on the popular PASCAL VOC 2007 dataset using conventional accuracy metrics such as the Intersection over Union (IoU), the area under the curve (AUC), and the proposal recall under various threshold values. We conduct performance benchmarking with the well-known Selective Search and Edge Box methods. Furthermore, we compare their requirements in terms of the number of proposals, computational complexity and model sizes to achieve such accuracy. Finally, we demonstrate by experiments that OPPHop outperforms Selective Search and Edge Box in both efficiency and accuracy.

OPPHop: A Light-Weight High-Performance Object Proposal Method

In the pursuit of Artificial General Intelligence (AGI), Machine Learning (ML) has achieved immense progress by exploiting parallel classical computing. Because of this success, our reliance on these techniques in autonomous systems has exponentially grown. However, throughout the scientific community there is growing concern of our increasingly rapid approach towards the theoretical limits of classical computation, better known as the end of Moore's Law. To continue these advancements, the adaptation and exploration of novel computational platforms is absolutely necessary. Biologically accurate, energy efficient neuromorphic systems that mimic the incredible efficiency of the human mind and the theoretically powerful optimization potential of fully entangled quantum systems are particularly promising arenas for enabling future advances. In this work, we compare these two flavors of novel substrate in detail by applying them to an identical challenge, and benchmark their performances in power consumption, time to solution, and reconstruction error against a classical CPU approach.
Our focus is on a subset of Hopfield networks, specifically, sparse attractor networks in which a uniform applied field is used to globally suppress activity. This encourages solutions consisting of a minimum number of active elements. We solve this sparse coding problem using the biologically inspired Locally Competitive Algorithm (LCA) on the D-Wave quantum annealer and the Intel Loihi neuromorphic spiking processor in two distinct ways, and compare the solutions to those found classically using Orthogonal Matching Pursuit (OMP). The Fashion-MNIST data set was chosen and dimensionally-reduced by sparse Principal Component Analysis (sPCA) under the constraint that a graph-based clustering metric (Henze-Penrose) remain unchanged. This enables the problem to be mapped identically to both devices. A sign flipped second data set was appended to the original to give each class a mean zero distribution, effectively creating an environment where the data could not be linearly separated. An early-in-time normalization technique for Loihi is presented along with analysis of optimal parameter selection and unsupervised dictionary learning using a local Hebbian rule for all variations. Studies are ongoing, but preliminary results suggest each computational substrate requires casting the NP-Hard optimization problem in a slightly different manner to best capture the individual strengths of the substrates, and the new Loihi method allows for a more realistic comparison with the D-Wave, while also drastically reducing the time needed to compute the solution with statistically improved reconstructions. Both substrates trail the classical approach to this problem in terms of reconstruction error, but D-Wave is the only one performing true binary sparse coding and Loihi provides immense energy savings along with viable solutions for both the full simulation and, especially, the early in time normalization variation.
The development of these substrates, and their use in expanding the machine learning parameter space, has multiple applications to Naval operations. For NP-Hard problems, our development of practical applications for quantum annealers lends itself to centralized decision making. For processing at the tactical edge, our progress in characterizing rapid neuromorphic solutions at low power are important in determining performance envelopes.


Brain Inspired Quantum and Neuromorphic Machine Learning

MetaTagger is an accredited GOTS ML test and evaluation toolset designed to streamline ground-truth data labelling and performance testing of imagery and Full Motion Video (FMV) based models. MetaTagger accelerates model testing to increase performance transparency, user trust, and model adoption. A smart collection of tools enable fast image labeling for training data purposes. Our custom-built tool called AutoTagger uses a Single Object Tracking (SOT) algorithm to automate object detection, labeling, and creation of ground truth datasets in less than half the time of manual approaches. Once Ground truth is created, FVM inference can be evaluated in minutes by simply uploading inference to the application and letting the MetaTagger algorithm work in the background. Once evaluation is completed, a filterable reporting tool allows custom reports to be generated and exported for future analysis. MetaTagger is the one-stop-shop for testing and evaluation of object detection and object classification algorithms.

MetaTagger - Computer Vision Testing & Evaluation Toolset

We approach the problem of image captioning with Cycle-Consistent Generative Adversarial Networks (CycleGANs). Due to CycleGANs' ability to learn functions to map between multiple domains and use duality to strengthen each individual mapping with the usage of a cycle consistency loss, these models show great promise in their ability to learn both image captioning and image synthesis and to create a better image captioning framework. Historically, cycle consistency loss was based on the premise that the input should undergo little to no change when mapped to another domain and then back to its original; however, image captioning presents a unique challenge to this concept due to the many-to-many nature of the mapping from images to captions and vice-versa. We have combined the state-of-the-art image synthesis model from Hao Zhang, StackGAN, with a modified version of Kelvin Xu's Show, Attend, and Tell image captioning model in a CycleGAN architecture to create a robust image captioning framework that is able to create diverse captions for each input image. Due to the duality of CycleGAN, we will also explore our model's capability to successfully generate relevant images based on an image description to test its ability to perform the opposite task and observe CycleGAN's ability at learning mappings between two domains from different mediums. We will be discussing our model's performance on the following datasets: Oxford Flowers, Caltech-UCSD Birds, and Microsoft Common Objects in Context (MSCOCO) datasets.

TextCycleGAN

With the vast amounts of unstructured data that is accrued through surveillance, body-cameras, mobile phones, humanitarian-aid and disaster relief, etc., there is a need to perform data synthesis to structured outputs through automated methods. Recent advances in machine learning have enabled compression of GBs/TBs of data sequences into short, compact, informal summaries as keyframes and/or video thumbnails and the capability to generate text that describes an overall image or full motion video has also rapidly increased. However, generating structured text beyond informal summaries, remains a relatively unsolved area of Natural Language Generation. This work will address this gap in the data summarization and document generation problem. This research would be the final component to an (non-yet existent) end-to-end system that could ingest data, summarize it, and generate a structured report.

Automating Situational Reporting

The presentation illustrates where machine learning applications built using transfer learning can be attacked, the means for carrying out the attack and some mitigations that can be employed. The elements in building and deploying a machine learning application are reviewed, considering both data and processes. Mitigations to these attacks are discussed with the engineering tradeoffs between security and accuracy. Finally, the methods by which an attacker could get access to the machine learning system are reviewed.

Threats to Transfer Learning Applications

The challenge of applying artificial intelligence to autonomous network security was investigated using Graph Convolutional Network Reinforcement Learning Methods in a dynamically-scaling computer network. A deep Q-network single agent method was compared to a hybrid method, which integrated a larger agent strategizing for the entire network with individual agents working on each node. The simulation network contained 'real' machines, to be protected, and 'decoy' machines, to act as deception mechanisms. An attacker pivoted through the network and the agents’ defensive strategies were tracked to determine which version adapted the best. These algorithms are being explored for this problem because of the changing action space along with the ability to adapt to the evolving network topology. 


Exploring Graph Convolutional Network Reinforcement Learning for Cyber Defense

Unlike current user emulators, D2U does more than simply replay previously-observed behavior, or call simple heuristics for stringing together patterns of actions. Instead, D2U provides the ability to generate unlimited, novel sequences of realistic behavior and orchestrate a plethora of virtual machines to enact these behaviors, thereby emulating a network of users. This ability to generate realistic, new behaviors sets D2U apart from all existing emulation technologies either in the academic literature or on the market.

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Next Generation NLP and AI Methods in the Navy Contact Center

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Real-Time Estimation of Threat Fields With an Autodidactic Data-Driven Sensing Aerial Vehicle

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